Creating Effective Self-Adaptive Differential Evolution Algorithms to Solve the Discount-Guaranteed Ridesharing Problem Based on a Saying

Sustainable transport is an important trend in smart cities to achieve sustainability development goals. It refers to the use of transport modes with low emissions, energy consumption and negative impacts on the environment. Ridesharing is one important sustainable transport mode to attain the goal of net zero greenhouse gas emissions. The discount-guaranteed ridesharing problem (DGRP) aims to incentivize drivers and riders and promote ridesharing through the guarantee of a discount. However, the computational complexity of the DGRP poses a challenge in the development of effective solvers. In this study, we will study the effectiveness of creating new self-adaptive differential evolution (DE) algorithms based on an old saying to solve the DGRP. Many old sayings still have far-reaching implications today. Some of them influence the organization of management teams for companies and decisions to improve performance and efficiency. Whether an old saying that works effectively for human beings to solve problems can also work for developers to create effective optimization problem solvers in the realm of artificial intelligence is an interesting research question. In our previous study, one self-adaptive algorithm was proposed to solve the DGRP. This study demonstrates how to create a series of self-adaptive algorithms based on the old saying “Two heads are better than one” and validates the effectiveness of this approach based on experiments and comparison with the algorithms proposed previously. The new finding of this study is that the old saying not only works effectively for human beings to solve problems but also works effectively in the creation of new scalable and robust self-adaptive algorithms to solve the DGRP. In other words, the old saying provides a simple and systematic approach to the development of effective optimization problem solvers in artificial intelligence.